Critical Success Factors of Analytics and Digital Technologies Adoption in Supply Chain

Critical Success Factors of Analytics and Digital Technologies Adoption in Supply Chain

Debasish Roy (AIMA AMU, India)
DOI: 10.4018/978-1-7998-3473-1.ch170
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Though the transformative impact of analytics on supply chain is beyond doubt, however, analytics in itself is not leading to improvement in supply chain performance, leading to ‘Productivity paradox'. This is of concern to practitioners as analytics is needed to improve supply chain performance to achieve competitive advantage for the firm . For researchers, it is of interest to develop a comprehensive framework to study the conditions in which adoption of analytics in supply chain successfully transforms performance. This paper proposes a research framework to study the “perceived benefits” and the “facilitating conditions” of successful adoption. Based on the research findings the paper tabulates the perceived benefits of analytics on supply chain performance, aligned to the SCOR model. Based on findings of “facilitating conditions” the paper proposes that presence of “Extended Supply Chain”, “Information System capability” and a suite of “Digital technologies” is necessary to harness the insights of analytics, for achieving improved supply chain performance.
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The digital economy has brought into focus the importance of Supply chain as a competitive advantage. (Li et al, 2006). Competition has evolved from firm vs firm to supply chain vs supply chain (Ketchen & Hult, 2007). This has encouraged firm to adopt analytics to improve supply chain performance. Impact of analytics, on supply chain performance, is an important topic of research (Kohavi et al. 2002).

The supply chain is a bidirectional flow of information products and money between the initial suppliers and final customers through different firms. Supply chain management includes planning, implementing and controlling this flow. Management scholars and practitioners’ agree that analytics has the potential of working on all areas of supply chain management to provide insights thereby improving supply chain performance. As demand and supply volatility has increased, the need for data and analytics has grown. A holistic analytics strategy may be the answer to many of the pains ailing the supply chain today (Cecere, 2016).

At a firm level, it is of interest to the Chief Executive Officer and Chief Supply Chain Officer, supported by the Chief Information Officer to explore what are the strategies to improve effectiveness of analytics in the supply chain. Research need to explore the effect of analytics on individual firm performance, an important factor that all firms are concerned about (Maskey et al. 2015). Harrington and Gooley (2017) opined that it is important to demonstrate the business benefits of analytics in supply chain initiatives to firms’ leadership.

Acceptance and use of technology by an individual or a firm is one of the most mature stream of information system research (Benbasat & Barki, 2005; Venkatesh et al., 2007; Venkatesh et al., 2012). Better means for predicting and explaining information system acceptance and use have great practical value (Davis, 1989). Identifying the facilitators to improve adoption of analytics in supply chain, using validated framework of acceptance and use of information technology, has both practical and theoretical value.

The Research questions are:

  • What are the facilitating factors for adoption of analytics in supply chain?

  • What are the perceived benefits of use of analytics on supply chain?


Background: Literature Survey

The existing research frameworks were perused for their applicability in this research and have been presented.

Technology Adoption model: Various theoretical models of Technology adoption have been proposed by researchers (Fishbein & Ajzen, 1975; Bandura, 1986; Compeau, 1999; Davis, 1989; Ajzen, 1991; Moore & Benbasat, 1991; Venkatesh & Davis, 2000) that specifies factors which influence technology adoption. The constructs of Technology adoption have been specified as performance expectancy i.e. perceived benefits on adopting the technology, effort expectancy or ease of use of adopting the technology, social influence to adopt or not adopt the technology, facilitating conditions, which positively or negatively influence adoption and behavioral intention to adopt the technology (Venkatesh et al., 2003).

SCOR model: SCOR model provides a systematic approach to identifying, evaluating and monitoring supply chain performance, covering the four core supply chain processes of Plan, Source, Make and Deliver (Jamehshooran et al, 2015; Stephens, 2001). In Plan, data is analysed to forecast the market trends for the products and services (Azvine et al, 2005). In Source, agent based information systems are studied that include evaluation, search, selection and price negotiation (Lee et al, 2009; Trkman et al, 2007). In Make, factors that facilitate production to be within specification and on time are perused (Ranjan, 2008). In Deliver, analytics used in logistics management to reach products on time are investigated (Reyes, 2005). Trkman et al, (2010) used the SCOR model to study Supply chain performance and introduced Process Orientation and Information Systems as moderators.

Key Terms in this Chapter

Extended Supply Chain: Is the BPR initiative, which assists in maximizing the impact of analytics in supply chain. Extended supply chain proposes a close integration with other lines of business units that are key in the supply chain such as product development, manufacturing, sales, and operations.

Business Process Reengineering (BPR): Involves the analysis and redesign of firms’ processes and workflows to achieve sustainable improvements in quality of response and cost competitiveness.

SCOR Model: Is the framework used for analysis and insights of Supply chain performance. It is used in this research for measuring the impact of Business analytics in supply chain.

Analytics in Supply Chain: Analytics can be defined as tools and techniques that are dedicated to harnessing external and internal data to improve supply chain efficiency.

Digital Technologies: Relevant to supply chain are Additive manufacturing (3D Printing), Internet of things (Machine to machine communication between devices that belong to different systems, including public infrastructure), Drone technology for logistics and Block Chain for transactions.

Unified Model of Adoption and Use of Technology (UTAUT): It is a consolidated model to study Technology adoption, proposed by Venkatesh et al. (2003) .

Big Data: Refers to the 3Vs (i.e., Volume, Velocity, Variety) of data which is captured for actionable insights.

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